Bridging silos and strategy in Pharma research with Shawn McKenna

‍

Description

What does it really take to lead successful pharma insights today?

In this episode of The Curiosity Current: A Market Research Podcast, hosts Stephanie Vance and Matt Mahan talk with Shawn McKenna, Senior Director of Analytics at Currax Pharmaceuticals. Drawing from experience across the supplier and client side, Shawn dives into the critical junction of data science, primary and secondary research, and business strategy.

From overcoming siloed teams to navigating AI’s promises and pitfalls, this episode is a masterclass in thoughtful leadership. Shawn discusses what agile really means in pharma, how to structure integrated teams, and why creativity and curiosity are the most underrated tools in modern analytics.

If you work in life sciences, lead an insights team, or are rethinking your relationship with AI—this one’s for you.

‍

Transcript

Shawn McKenna:

I think that's just regular human nature, that if you're really good at something and somebody else tries to come and do your thing, regular human nature is just like, why are they doing what I want to do? Why are they doing my job?

‍

Stephanie Vance:

Hello, fellow insight seekers. Welcome to The Curiosity Current, a podcast that's all about navigating the exciting world of market research. I'm Stephanie Vance.

‍

Matt Mahan:

And I'm Matt Mahan. Join us as we explore the ever-shifting landscape of consumer behavior and what it means for brands like yours.

‍

Stephanie:

Each episode will get swept up in the trends and challenges facing researchers today, riding the current of curiosity towards new discoveries and deeper understanding.

‍

Matt:

Along the way, we'll tap into the brains of industry leaders, decode real-world data, and explore the tech that's shaping the future of research.

‍

Stephanie:

So whether you're a seasoned pro or just getting your feet wet, we're excited to have you on board.

‍

Matt:

So with that, let's jump right in.

‍

Stephanie:

Today, we're excited to welcome Shawn McKenna, Senior Director of Analytics at Currax Pharmaceuticals and a seasoned expert in pharma market research, advanced analytics, and commercial insights.

‍

Matt:

Shawn has built an impressive career spanning both the supplier and client side of the industry, leading advanced quantitative research at Adelphi, driving insights at Jazz, pharmaceuticals, and now shaping the commercial analytics strategy at Currax. His expertise covers everything from patient journey analytics to integrating data science into market research teams.

‍

Stephanie:

In this episode, we're diving into some of the biggest challenges and opportunities in pharma research, how companies are balancing primary and secondary data, the evolving role of AI, and what it really takes to structure an insights team that seamlessly integrates data science, primary and secondary research, and business strategy.

‍

Matt:

If you are curious about the future of pharma research and how technology is reshaping the industry, you won't want to miss this conversation. Shawn, welcome to the show.

‍

Shawn:

Thanks, guys. Happy to be here. Thanks for having me.

‍

Matt:

Of course. We're very happy to have you. I kind of alluded to it in our intro, but you've seen a lot of different perspectives in the insights and data analytics universe, all as it pertains to pharmaceutical research. How has your work on both sides shaped your approach to research and insights? I know that's a question we like to ask folks who have this kind of dual perspective. What are the particular challenges or opportunities that you've recognized sort of switching sides of the fence?

‍

Shawn:

Yeah, I think it's a great question. It's a common one, but I think it's a really great one, right? Because folks on the supplier side might eventually want to do stuff on the client side, and folks on the client side might wonder what it's like on the supplier side if they've never actually done it. I think the main takeaway that I have after doing both of them is just having a recognition for the limitations of what primary research can do. Not everything can be answered, no matter how much everybody wants it to be answered by a survey or a qual. I just think it's good to know that you can have really great questions and you can get them pretty far along. But on the supplier side, you can only really do so much sometimes. I think it's really beneficial to focus on one big thing. And do that really, really, really well. One big question, instead of like the 32 questions that client me might have. Because you get caught in the trap sometimes, and I was caught in the trap sometimes of you want to try to please and you want to try to answer everything. But sometimes that's just not possible.

‍

Matt:

Yeah, that really resonates. I know both Stephanie and I spent time on the client side as well. One difference I always feel is like you're on the agency side, you have maybe an increase in breadth, which is nice. You have a lot of different topics that you can tackle, a lot of different challenges and verticals, and you work with a lot of different teams. But you don't get to go particularly deep in any one particular strategic solution. And sometimes you get taken away from the implementation of the work a little bit early on, which is sad because we're all research nerds and we love studying people. And you kind of miss out on the implementation of the work that you do.

‍

Shawn:

And I just think that's more contingent on team client to involve everybody as much as possible. I think that's also the main thing. Like nobody on the supplier side actively wants to do a bad job. You really do a great job, but you can only do as good of a job as the information you're given and as included as you are. It's just, I think, contingent on team client. Really involve folks. I mean, those are my favorite clients from when I was a supplier on the agency side are the folks who brought me along all the way through. And I just strive to hopefully do that. Hopefully if any of my friends and suppliers are listening, they would agree.

‍

Stephanie:

Another question we have is, so it's a bit unique that you've worked across primary and secondary research, forecasting, omni-channel analytics, patient journey mapping. In pharma, I think like in a lot of industries, we think of data as just being so fragmented across a lot of different stakeholders. What do you find is the biggest challenge in creating an integrated insights function like that? And how do you see organizations overcoming the types of silos that can occur with like your primary research team and your secondary research team and your journey mapping, you know, function that might sit in a totally different department?

‍

Shawn:

I think it's really hard first is just recognizing that it's hard and that there's one size or one magic solution won't be the same for everybody. People like to work in silos. Silos are somewhat comfortable. But if you and if somebody crosses those, it sometimes feels like you're being attacked and you might need to defend that. I think that's just regular human nature that if you're really good at something and somebody else tries to come and do your thing, regular human nature is just like, why are they doing what I want to do? Why are they doing my job? And that's sometimes just scary. I don't think there's many magic solutions besides just hopefully being an interested and open individual that can hopefully recognize and understand that people are different and that people want to work on different areas. People work differently. And then communicating and sharing is pretty big. And then just a recognition that if you approach other departments or you try to work across silos or break those barriers down, there's a really big difference between saying like, I'm right and you've been doing it wrong versus like, here's something I thought of. What do you think it could be wrong? I don't really believe, Stephanie, there's like the magic solution to how you break it down besides just having a really good group of people who are really open and willing to work together.

‍

Stephanie:

That makes a ton of sense. Another question that I have. So on the supplier side, I personally have worked across a lot of industries in my career, but not pharma, critically. And I have this perception that pharma research is highly constrained by regulatory and compliance challenges, which I would think would make it harder to move at the same speed as we are seeing other industries be pressured to move at. So I'm curious about how you deal with the challenges of developing strategies that are agile enough to provide timely insights, right, at points of time, right, while also still maintaining that rigor that you have to have in pharma.

‍

Shawn:

Well, the fun answer to this question is that because I've only done pharma for nine-ish, ten-ish years, and then two or three-ish years, and then three-ish more years now, I actually don't really have a great concept of how quickly y'all can do things. So maybe my just whole expectation and my whole frame of reference is a little bit different for what I think is quick. Personally, when it comes to agile or quick, I personally think it's very reasonable to think about quant in terms of, all right, I'm going to write an RFP, give folks time to come up with a good solution or proposal, evaluate and commission in a week. Then we're going to write the thing in a week. Then we're going to field in one to two weeks, depending on incidents. Then we're going to top line in three to four days. And then maybe we have some fuller report a week later. I mean, that's a month. That's month and a week. That's not awful in my personal opinion. And then if we do qual, you know, we can still knock out the same upfront bit, but then we just have a little bit shorter because we can recruit and then we can field on an ongoing basis. And then as long as everybody's comfortable with maybe relaxing the recording requirements, that it doesn't need to be this amazing over the top PowerPoint, but it could just be bullets at the end of the interviews or the days and we kind of build and iterate. I mean, now I've just shaved a week off of, say, equal. So I know maybe you guys in Team Consumer or the folks out there in Team Consumer, like a week, you know, a month, a month and a week. That's so long.

‍

Stephanie:

Yeah, we're hitting a week these days. Yeah, it's bananas over in Consumer World.

‍

Shawn:

Yeah. And my personal perspective is sort of like, would that be amazing? Yeah, sure. It would be really amazing. But if we have a question that I think needs to be answered by hearing the perspective of people living with conditions or healthcare practitioners, if it's that critical that we really need to hear from them and we can't make an educated decision, I'm okay waiting a month to make sure we get that answer done very correctly and very accurately and very well because it's a really important decision that we might have to make. That's my perspective on it. If it's something small and something quick, and also pharma doesn't really move that fast, right? We have strong regulatory presence for really good reasons because we don't want to do anything incorrect or harm anybody. We want to make sure things go as well as possible to make the best possible decisions. And if we can't wait a month, a month and a week, I think that's kind of a failing on our planning side versus the ability to need to force you all or suppliers to like break their bounds.

‍

Stephanie:

Yeah, that's such a good point. I think in particular, your point about special audiences is such a critical one, right? It's not like you're going out to Gen Pop 18 to 65 to find out about a shampoo scent. That's right. Yeah.

‍

Shawn:

That sounds fascinating.

‍

Shawn:

It's fun.

‍

Shawn:

But I don't think I've ever actually, I can actually count the number of times that I've been involved with Gen Pop on a sample basis. I think there's maybe three times.

‍

Shawn:

Oh, wow.

‍

Shawn:

It's been 12 years that we've really genuinely gone to Gen Pop. It's just not the same when, as long as we adjust our expectations. My personal feelings are, if a month is too long, we've kind of messed something up in planning and in thinking it through.

Almost everyone's team supplier and not trying to make them do things super quickly.

‍

Stephanie:

Hey, we'll take it.

‍

Shawn:

That's right. We love that.

‍

Matt:

Speaking of speed, my role on the podcast is to make sure that no guest escapes without talking about AI. It's my show of fealty to our technological overlords. AI has been positioned as a game changer for everything. Certainly, I'm sure by some, including in your industry and pharmaceutical research and healthcare in general, from a lot of different perspectives. You've described it as being in an inchoate stage. So, you know, not quite fully developed yet, still requiring a lot of human oversight. I was wondering if you could just talk a little bit about that. Like, where do you see the value of it? So today... Is it going to change? Are there areas where... AI might play a role like maybe, you know, in recruiting for special audiences, something like that. Are there particular areas where it's going to work better than others? But overall, what's your take on AI and pharma?

‍

Shawn:

Yeah. So in pharma overall or in pharma for certain market research insights?

‍

Matt:

Well, I'd be curious on your perspective on both, really. I sense you have an opinion on both.

‍

Stephanie:

Well, yeah, it seems like it's really exciting in pharma overall. I would love to hear you talk about both too.

‍

Matt:

Very optimistic.

‍

Shawn:

Pharma overall, I think great. Now, mind you, my area of expertise is definitely not medical-ish things.

You know, I would defer to some of my medical friends and colleagues and experts who probably know a little bit more. But, you know, when it comes to the ability of AI to intake a lot of information and then provide it back in a usable or an interactive way, or when it helps to trying to find patterns in clinical data that may not exist, or, and this is so far beyond my understanding, but when it comes to things like drug discovery or things like that, I believe those are really wonderful areas because AI is fundamentally really good at pattern recognition. So patterns that we couldn't figure out, no matter how good I think I am at it, are an intake of data and synthesis and then response. I think I might be good at it, but I'm not as good at it as some of these things that are being thrown out there now.

When it comes to commercial aspect of things, on the insights aspect of things, or survey data, personally, I've not seen, and I'm probably opening myself up to about 38 cold calls on LinkedIn. I have not seen incredibly compelling use cases. Using NLP to code OE is not really new. There were papers on that from the early 70s, mid-80s on the use of NLP, those sorts of things. That's not really a newer novel. It can be done more quickly and more thoroughly, perhaps, but that's not really new. Using it to check for patterns of bad respondents, that's kind of new and that's kind of interesting. Using AI to design surveys, I don't really, or write questions. Could it get some of the basics out there? Sure. But at the end of the day, AI is not going to understand the new, or in my opinion on what I've seen, AI won't understand the nuances of like. EGFR positive drugs for lung cancer and the relationship of some of the newer data to some of the older data. To use like a completely random example has nothing to do with my current job. But, you know, I just don't think. It would be able to pull out exactly what some folks might be interested in that realm. On the bright side, do I use AI on data and commercial stuff? Yeah, sure. I mean, we just used it to put 50,000 to 75,000 drug codes into categories that made sense for our business. And AI got us pretty far. It saved me a bunch of work. But somebody had to sit there at the end of it and kind of scan it and go like, wait, is that drug actually in this category or not? And until you get to the results end of things, you don't really know, right? So there still is a supervision level that, the current hot topic for what I've seen more recently in market research or primary is simulated or synthetic data, whether they think about that in terms of qualitative responses or even simulating quant. If anybody has a really trustworthy and real assessment of it, I'd love to see that. But what I'd love to see is you run the same study with real healthcare practitioners or real patients. You run the same one with what you purport to be simulated or synthetic. We get input on the design of the surveys because sometimes those survey questions could kind of be very simplistic and adaptive. So then you have very comparative answers. And then we get the raw data and then we know how it's trained on it. We did all that, you can maybe win me over. But at the end of the day, I personally think I'm going to still take two or three people from a trusted market research company who I know are clever, insightful, intelligent people and have been doing this for a while, say where I used to work, or say another company, Hawk Partners in my group. I'm going to take them over a computer program from somebody who thinks they know it better. Because these people have been doing this job for five, 10 years, and they're very good at it. And they know what we're after, and they know what's important and what's not important. That's very important and very critical for me. So I'd love to see AI get to this level of helping understand data that might come back in surveys more than just say summaries or frequencies. If it could do that, that'd be wonderful. But hit me up on LinkedIn. If somebody's got it, I haven't seen it. So I'd love to see that.

‍

Stephanie:

I think that's such an interesting point you're making because a lot of what you're talking about really is it's driven by your experience in the very like specific vertical that you work in. And like what the kinds of like when we talk about survey design and AI assistance in the world of survey design, it's actually quite good, in my opinion, at writing a consumer survey about like a concept test or innovation work. And right. Like that's. That more formulate type of research where, you know, it's written in consumer language about something that, you know, AI models have been trained on quite a bit. But it's such a good point that like what you're doing is a lot more nuanced and the language that's used in that vertical is a lot more nuanced. And it would be like writing a really complex B2B survey and expecting AI to do it without any intervention.

‍

Shawn:

The devil's advocate position would be that I'm... Speaking as if I know everything and there's somebody out there who is doing it. And that I also am coming from a position of alleged superiority or exclusivity where, you know, really my stuff isn't that much different. So why can't we get away with it? That might be true, but I, or somebody could argue that position rather. I just personally do believe that when it comes to design of a survey, it's not going to get the exact wording correctly. It's not going to get maybe the topics correctly on a clinical trial or opportunity design. It's not going to phrase things the way I might want to phrase them so I can be respectful of people's lived experiences.

And I just think that's a pretty important thing for folks to keep in mind.

‍

Matt:

The human in the loop concept is, you know, it's always part and parcel with any conversation on AI. But I feel like... When you're talking about situations and industries where human empathy truly are really critical, they're core to what you do. And it's like, you know, healthcare is just such a great example of that. That human in the loop plays an ever more important role. It's hard to imagine getting to a day where that's not the case. You mentioned synthetic data, which is obviously a big topic right now. It's interesting to hear that come out of pharma. Certainly, it's a big topic from other industries, other verticals. It just seems like there's this sort of psychological barrier that people run up to when you start talking about basically total control of the interaction, total control of the insights and the data that is just like fundamentally different than a conversation about drafting a survey or helping me to understand what the open-end results look like. There's something, there that appears to be different. I'm from Detroit, so I think of everything through the lens of the automotive industry. It's like you think about self-driving cars. They haven't been adopted nearly as fast as was originally predicted because there's just this psychological barrier about giving over total control of the vehicle that you, your family, your children are in. That's different from, say, letting your car help you park, which has been out forever and was like immediately adopted. People were like, heck yeah, help me parallel park this car. That's a challenge I need. I need solved. Help me brake if there's a stop occurring that I don't see. Adopted immediately. So you saw all these like autonomous systems just take the industry by storm. Self-driving was really somewhat of an incremental change on that. But yet at the same time, we're not all kicking back, reading the newspaper or scrolling our phones while our cars drive us somewhere, even though the technology is there.

‍

Stephanie:

It feels transformational, right?

‍

Shawn:

Not incremental. Yeah.

Yeah, I think... It's pretty asymptotic, right? Like you can get things pretty far in being really advanced and quicker and quicker and quicker. And then you start making little tiny incremental gains in the quickness to be able to do something. And if we're going to sit here and say, all right, you know what? You don't need to get insights from real human beings. It can all be simulated. And the way we're going to do this is you're going to ask a question to a set of data that our archetypal defined sample is going to live in this mystery database. The mystery database has been trained on a mystery number of large sets of consumer information before. So we know how people will react to this historically. And all you need to do is ask it a question and it'll tell you the answer and then you can move forward. Like you said, Stephanie, perhaps that might work in, say, a shampoo fragrance or what people tend to want or what people in a certain area or a certain group of people, men versus women or what they might prefer. It's not, I just don't think it's going to work so hot when I'm talking or want to know about the future of a certain area of healthcare, and what someone might do or not do. It's a lot more layered and emotional. And I think some people out here listening, if they do pharma stuff, they might go. Well, yeah, but historically, again, the devil's advocate position would be like, no, it can be reduced to a formula. It can be, you know, if the drug is first to market, it's going to do so well. If the drug is second to market without a declining advantage, it's going to do so well. But I think there's plenty of examples where that hasn't been the case, that to trade it all as one uniform block, that a group of synthetic or chain data on historical people could do just as well as real people. I don't know. I'm personally not super convinced just yet when it comes to healthcare. I think you can get super close, like you used in the car example now. You can get really, really close to something that is automated or something that helps streamline or something that's more efficient. But people aren't just going to... I'm not going to jump in the fully automated car because I'm scared to do it. Because I'm trusting things that have happened four or five years ago. And I'm trusting that the company's survey data is representative. And I'm trusting that it has similar responses or built on the same way to what might happen in the future. That's a lot of trust for the savings of three weeks. Right? That's that trade personally that I run through.

‍

Stephanie:

Hear that. Well, to move on from the topic of AI, which always is a big win for us just because of its prevalence right now. Another, you know, to broaden out like our lens and time a little bit, one of the bigger shifts we've seen in the past decade is the blending of data science with traditional insights modalities. And it seems that you've built teams that integrate both. And I'm curious if you could talk a little bit about what it takes to create a structure where data scientists and insights professionals work seamlessly together. And we talked about that a little bit in the silos question, but I'm just like, how do you build that team?

‍

Shawn:

First, to rephraise, I like to think I've been a part of teams that have done it well. I don't think I can take all the credit for everything. I think that would get yelled at by my friends and colleagues. To answer your question, when I worked at a certain place, it was, it's a lot of what we already spoke about, Stephanie. It was, no, no, no, you handle the primary. Somebody else will handle the data aspect. You stay at it. Then it was kind of. You know, no, no, no, you live over there on commercial insights. What happens maybe afterward is not necessarily your thing. If you do that, you end up answering just little bits and pieces of the puzzle instead of trying to work it all out. And I think as long as you approach things in an open and transparent way on why you want to involve or certain things. I personally do optimistically think people might be more open to that. So how do I approach it? Or how do I like to think I try to approach it with people that I work with, friends that I work with, or former groups? Start at a big problem. Figure out everybody that's involved. Involve everybody that's involved. Break it into little bits and pieces. Understand that you're not the one who might know better than anybody else. But at the same time, you know, be open to somebody who might have a better idea than you. Figure out as much information as you have internally available on your internal resources. Figure out what you can buy from Numbers or Data. You can't do those two steps. Then you need to talk to people and answer questions. Then you do that. And you involve all of the stakeholders all the way through. I do think it just comes down to clearly identifying a problem, clearly identifying those folks who are involved, making sure everybody, the experts are involved. It takes just time, effort, communication, a willingness to be wrong and say that you're wrong and somebody else has a better idea. And then move forward from there. I personally just think those are ways to work that lead to better outcomes.

‍

Stephanie:

For sure. I'm curious if like, so certainly the collaboration between different team members who work in different areas, are you also seeing that like just generally upskilling, like for instance, a primary researcher to be able to do like some big data querying and to be able to sort of like work across modes. Is that something that becomes more important where, I have to be more of a Swiss Army knife of a researcher and an insights person, or does it still feel like, no, these are different skills and it generally works best for them to remain different skills, but we just need to collaborate and share across the skillset and roles.

‍

Shawn:

I think it would be fantastic if... Folks did the former. I have R and SQL open on my computer right now alongside a survey. I mean, it's-

‍

Stephanie:

Perfect. Yes.

‍

Shawn:

I do think it's beneficial. To have a general ability to work in those things. You don't need to be an expert in it. Not everybody needs to be able. To write really complicated Python or Databricks or anything like that. There are people that are going to be very good at it. I do think it's very beneficial for primary and focused individuals to have a general understanding. In healthcare specifically, I do feel it's very beneficial for primary focused individuals to have an understanding of claims, numbers, data, sales target, those sorts of things. I just think it makes people stronger. And conversely, I also think the more technically focused. People, sometimes very technically focused people sometimes think The technical fancy stuff is the cure-all and answer to everything.

That's not true, right? There's going to be gaps in information behind the scenes, and you do need to just hopefully sometimes pick up the phone and talk to people that can fill in. Excellent gaps, or sometimes you need, you can get something as far as possible, but there's going to be really concrete, identical gaps. I work in an area right now where there is just something fundamentally that you can't. Bye. Data on. It's a bit of a mystery. So how do you fill that mystery? We're doing some patient qualitative and patient journey work to try to fill in the gap that we know is out there. And then you just try to make the most of it. So yes, Stephanie, I do feel it's very beneficial to have experienced most.

‍

Matt:

That is actually something I wanted to talk a little bit about. And I know I don't want to ask you to spill any proprietary beans here, but having done a stint in pharma myself, I know one of the things that is challenging is that patient journey is so critical. I mean, other verticals will talk about a consumer journey. It's important. It is core to the work that you do, I'm sure. And yet it's so hard to fill those gaps just because of the discrete nature of the population you might be interested in. And yet the need to go so deep, you know, it's not just that there is a limited sample, but you need to find this true depth of insights and knowing and understanding. How do you do that? That's a challenge you alluded to. How do you kind of pull all these pieces together and draw that picture for your stakeholders?

‍

Shawn:

Sure. I think, I don't think it's necessarily proprietary. I think generally... If you have a lawyer, an area with it. In my opinion, if you have an area with a more well-established treatment paradigm. So by that, I mean the first thing folks might get from a doctor is this. And that's generally true for 90% of the people who have an issue with this. And the issue with this, the reason they get it is because it's been around, it's proven, and it's really a great option for folks who suffer from... Blah. Then once blah stops working for whatever reason, or some folks just don't have a great experience with it, you move to this or this. But the reasons you move to this or this are because of you tend to move to this one. If you didn't have a great experience on say side effects or tolerability. Or you tend to move to this one if folks, unfortunately, they aren't responding efficaciously. It's not working for them really well. If you can, and then, okay, now we get to, say, a third line of... Treatment where all of a sudden, let's just say there's one big thing left over. And, you know, maybe to make this maybe a little bit more concrete, we could kind of talk about maybe like a biologic, like your Humira or your Enbrel or, you know, those drugs, which are fantastic for folks. Sometimes those drugs might happen earlier, but let's just pretend those drugs happen after we've exhausted all the other stuff. If you can really concretely kind of rattle off how that'll play out. And quant can do the trick. All right, what proportion of people? And we're going to survey a bunch of people, and we're going to survey a bunch of people who have tried this, this, this. And what we want to know is their treatment history and how long they were on those things. Or we could do ask doctors to talk about patients and what might happen. Cool. You can do quant. Flip side is let's talk about Quell or let's talk about more interactive things. When we don't know. That treatment paradigm or it's evolving or it's changing or we're trying to look to the future when stuff might happen so we're trying to make people guess So then we're going to talk to healthcare providers and we're going to talk to folks living with this, both experienced and not experienced with treatments in it. And for the folks living with it, we're going to play a bit of a what if game. You know, what if this had had. For the folks not experienced, we might be asking them to kind of lay out how they might do it. Then we're obviously going to talk to probably really high up doctors. Folks who are going to lead the opinion and then folks kind of in the middle ground too, who might be on the day-to-day practicing. Say, all right, all right, two years from now, and this, this, this, this, this, this. What in the world are you going to do? That's where we're probably going to verge a little bit more in the Quell. Quant? I think could be done. But to your actual question about patient journey. We can do it primary through primary through Quell. We also have longitudinal data available on what folks are doing, but there's that, what that's going to miss is the why. So like we quantify pathways, but I don't know why somebody did this one to this one. I have no clue. Right. And then we're going to have to talk to people in that example to like fill in that. Well, why? Why did we read this to this? Or why is this changing so quickly? So it's a little bit messier, but it is something we care about. And then the last thing I'll add, Matt, is just folks, though I think everybody wants always to go back to research to fill up, at some point there is a bit of just logic or common sense on this that we can fill in, you know? Why do- People not want to do something. People don't want to do something for pretty solid reasons. There's something that, something that didn't really work so hot. Some things cost a lot of money. The reasons why some people don't want to do something, right? Crazy, unique, or different, or novel. And sometimes just common sense can fill in a little bit of the gaps as well.

‍

Stephanie:

That's interesting. It feels a little bit like you're building almost like decision trees, but like through disparate kinds of data, right? Because you don't always need to do like a regression-based decision tree. You can be pulling in these elements of like common sense, qualitative feedback, and then also, like you said, that just host of like longitudinal data that you maybe have access to.

‍

Shawn:

I think for some of my friends who know me, it might sound like heresy or like, what am I talking about when I'm the one saying like, sometimes the numbers can't do everything. But I do think it's true that at some point, would I love for it to be super perfect and super clean and super neat through some crazy numbery thing? Yeah, I would. But it's not going to always work. You know, like... I will make the most complicated. You all know Sankey charts or flow charts?

‍

Stephanie:

Oh, yeah. We love a good Sankey chart.

‍

Shawn:

I will make the most complicated Sankey ever. I'm the only one who cares about the most complicated Sankey ever.

And I'm like super pumped about it. And then I would be like super into it. It might not tell the whole story as much as I'd love it to.

‍

Matt:

Sadly, Sankey is not the answer for everything.

‍

Shawn:

He's not.

‍

Stephanie:

If only.

‍

Shawn:

I would love that.

‍

Matt:

Need a Sankey chart for life.

‍

Stephanie:

You have mentioned in a past interview that, I love this, by the way, the hardest part of research is knowing when to stop, knowing when you've truly exhausted all of the secondary and internal data before moving into primary research, which is, I think, a good transition because we were just kind of talking about when you need to supplement versus not. But can you walk us through that decision-making process? How do you know when it's time to shift approaches?

‍

Shawn:

That depends on, if we had unlimited research, I think research, will expand to fit the resources it's given So, from that client side if if we have unlimited resources we would do unlimited research just because it helps us and helps people feel more confident in their decisions So first, I just think operating in a little bit of a more constrained universe in general is pretty solid. You know, okay, there's some new question that's being asked. Than we Have we touched on that in the past? I feel like that's super over, luck.

‍

Shawn:

Totally is.

Yeah. I have a pretty funny anecdote from up, buddy mine who did the commissioned research synthesis for things that I did, who's now one of my buddies and my suppliers now. He inherited all of my primary work when we were competitors. So, first, and then he was basically saying, I think somebody's already answered some of these things. So first, I think just a thorough understanding of the historical information available and what might have been done just because it was done two or three years ago or just because it was done by somebody else doesn't mean it's bad. Doesn't mean it's wrong. Doesn't mean it's inaccurate. You might disagree or you might think you have a sense of superiority over somebody else who did something differently. That's your own feelings about superiority, and they might be valid, but chances are there's something in something that was done before that has already touched on this. So first of all, I think just understanding the historical information available. Second is then you move into why. Why does your different team need to know this answer? Why? What's the reasoning and what's the rationale? Now, is it nice to know, or is it super critical for a real... And valid decision that you guys absolutely must make. Because if it's a nice to know if, or if it's confidence builder, If it's an actual real unknown that we can't answer, first of all, through historical stuff, second of all, we just can't common sense think about it. All right. Cool, now we've moved into the next level. There it is. Well, then do we have any stuff that we've already purchased that can help point us in the right direction or not? That's usually clear and simple. Yes or no. If it has to do with, Stephanie, you mentioned concepts. If it has to do with new concepts or new imagery, chances are numbers that you buy internally or have, that's not going to help you because now you're looking at something A, visual and B, that's new and C, that's more of the future. All right, cool. We've never done it before. We really need to know it. And there is nothing internal that we already have available to us on a numbery day to basis that can actually do this because it has to do with the future and it's critical to the business. All right, cool. Now we need to talk to some people and get some actual real input on it. But I think just ruling out historical. Things you have already bought or things you've already purchased, and then really whittling down whether that question is. Severe enough to warrant something that you need to do. You now got boom, boom, boom. All right, now you're in the realm of... What do we need to do? Quell and then into quant for, say, concepts. And that's usually how I try to do things.

‍

Stephanie:

And is that a flow that just like your team internally goes through? Or is this sometimes something that you're having to communicate and walk an internal stakeholder through, like who comes in through a request? And you're like, I hear the request, but I want to take you through what we already have, what, you know what I mean? That same process. And then because that to me is a little bit more challenging because you're, it's cross functional by nature and you're having to educate someone and, and persuade them that we do have some existing foundational knowledge to start answering this question, which can sometimes be a harder task than figuring it out yourself.

‍

Shawn:

I might, first of all, I might be biased. I think at Jazz, I've worked with incredible people who did already go through a lot of that process internally. I think our two main brand leads are wonderful people who did a really great job of thinking this through. I think in my current position, I think, yeah, we, the marketing team that I work with, I think also does a really incredible job at sort of running through that themselves before we get to May. So I do think I am in a very fortunate place. However, when I was a supplier... I do think it's a very... Worthy conversation to have. With. Somebody Also, another caveat on that original two points is... I am a little bit. Fortunate in that I've been doing this for a little bit. People Whether I do or I do not, I don't know. But people always think I know what I'm somewhat doing. So I think that helps. However, back to the supplier side, I think if you get a question and you're just sitting there over there going, are you serious? And it, you know, tend to typically might come from, let's throw new people, new me under the bus. Let's throw me under the bus. Because I was reactive and I wanted to please and I didn't know that it was available. And then you're sitting there on Steam Supplier and you're like, hey, if I actually put this out to this person... I'm turning down, money.

‍

Matt:

Yeah, it's the conflict.

‍

Shawn:

Yeah. Which is a huge Catch-22 because you're basically turning it down even though you're sitting there and you're like, oh, come on. We did that two years ago for so-and-so. And I actually already know this. So then you're stuck. My, again, my altruistic, I like to think it's altruistic, but really it's just out of an overwhelming sense of obsessiveness and anxiety. It's just, if I've done something before on the supplier side saying that, I still think it's fantastic. When I first joined. Prior company that I worked at. Two main groups of people that I'm still very close with at competitor companies basically told me two or three things. I was like, no, you don't need this. This was done before. We did this like a year ago. I was like, thank you.

‍

Stephanie:

I was going to say, I've never met a client who wasn't grateful when you do that action. Right. And so I think for a client relationship purposes, it's always a win.

‍

Shawn:

It is. It really is. Because I think it's a sign of, you know, it's honesty and it's respectfulness. And I think I'm eternally grateful to the... Two groups of people that told me these things when I first joined them, already said their company names. And I just am very grateful toward how they were able to fill in things historically to help. To answer your question, whittled down what actually needed to be done because it was a new question. First, it was just me coming into it.

‍

Matt:

Yeah. And I think it, you know, going back to Stephanie's point on, you know, clients being grateful for that type of interaction, like that just speaks to how things have changed, continue to change on the supplier side. It's like we know it's no longer enough to just provide the answers. Like in this world where everything's changing so fast or all these new tools, it's like the way you really show value is by curating that data into like knowledge and implications and helping clients, whether they're like internal or external, really like see the forest through the trees, including if they've done the, if we have had done this work in the past, like that's like that like information management is a role that I see people taking more and more of an interest in.

‍

Shawn:

That's really well said. I think that's critical.

‍

Matt:

So kind of like moving into our closing questions, I have one question we ask all of our guests here, which is to prognosticate over the next five years. So getting back to all of this, all this change, like we talked about AI, we talked about the pace of evolution and just all of the different challenges that are presented to you, particularly in a challenging industry to do the type of work that you do. What is the skill or mindset? That is going to be the most valuable to someone looking to do the type of work that you do? You know, if you were looking to hire someone for your team, what is that skill or mindset that you would look for that would show you that they are setting themselves up for success over the next five years?

‍

Shawn:

To Stephanie's earlier question, I am a bit biased towards technically inclined folks, or at least folks who demonstrate a willingness to do that. Mindset, I think, is more interesting and more important. I think it's important to have folks who are generally creative and more curious and able to communicate clearly the limitations of what they've done and impact it might have on the business. I think creativity is something that can never really be replaced or really thought through or think differently. Creativity has a downside, right? Because I could be creative and I could be unique and I can think about all these, or I like to think of them, where I can always try to come up with all this stuff, but then I can strike out like 15 times. And by striking out, I mean literally spending hours over a year working on something that... Somebody said, which might be important. Or us, and they don't know that I've spent six or seven hours trying to figure it out. And oh, by the end of the day, I... Take the average of two different sets of people and the numbers are 1.6 and 1.65. And I'm like, good God.

‍

Matt:

Absolutely. Waste that more time. Yeah.

‍

Shawn:

So that's the downside of the creativity is spending an inordinate amount of time and being very obsessive about things. However, on the flip side, when the creativity or the, you know, that sort of mindset or curiosity works, it's very impactful and it's very helpful. So I do feel technical stuff is always a little bit important and I am biased toward it, but I do just think more importantly, I just think curiosity and creativity will win out over a lot of other things I do.

‍

Matt:

That's a great answer. Again, I mean, I think it's really on the theme with a lot of the conversations we've had. It seems to be kind of a universal truth in that, which I really like. I look for, I like a simple universal truth.

Getting into a philosophical space now.

‍

Shawn:

That's right.

‍

Matt:

Well, speaking of philosophical questions, Stephanie has a deep philosophical question for you that we need to, that we can't let you go without addressing.

‍

Stephanie:

It's true. And we don't usually end on a totally out of pocket question. But Shawn, you showed up on my doorstep today and I have a burning question that takes up way too much mental space for me. Where do pharmaceutical names come from and how are they tested and vetted?

‍

Shawn:

So interestingly, first, I think I've only been a part of a very small amount of these or even tangentially involved in them. Most of my stuff has to be more with commercial and opportunity afterward. However, so I'm not a complete cop-out. To the best of my knowledge, first, giant list of things brainstormed. How those things are initially created, I think it's just we can't be too duplicative of something else. We can't be outright or explicitly promotional in the name, those sorts of things. And then as more and more of these names have promulgated over time, it becomes more and more difficult to be unique, different than them. So I think a lot of these names, a lot of these sets of letters gets generated. A lot of questions on folks' associations with those things. Positive, negative, what do you think about? Then a lot of diligence to make sure we're not unduly violating anybody else's names or we never want to promise a benefit that has not been clinically demonstrated. So we can't have those associations either. So I think it's less interesting than it is just ensuring we abide by the correct rules and regulations regarding how things proceed here. Admittedly, though, it's not really something I've been super involved in except once or twice, but that's how I've seen it play out. At the end of the day, we just want to ensure that anything we put out there, any end product once marketing has its way with it, is fair and balanced and genuinely demonstrates what we believe to be true about the clinical value of the product. And we never want to confuse or dissuade or change anybody's or trick people or promise something that isn't true. So that's the fundamental nature of what we do. So it's more on the diligence aspect of things.

‍

Stephanie:

Yeah. I think we don't see that as much in consumer naming of other, right, of other types of-

You guys can call anything anything, as long as there isn't a fringe on another name.

‍

Shawn:

It's almost completely different. It sounds like, you know, like, you're abiding by regulations that say it has to be unique. It can't be descriptive, really, of what's in the drug or what it's meant to do. It can't be overly generalized. It's got to be a challenge. And then to your point, it's really interesting to hear that it's gotten so to the point where there are so many unique names out there. It's hard to make a name unique because they all just sort of start to sound like the same level of randomly generated syllables.

‍

Stephanie:

I feel like we've really leaned into the letter X as the letter to start drug names with.

‍

Shawn:

There's only so many letters left.

So you got to get there. Yeah. And if it's changed, the last sort of naming-ish thing that I was sort of kind of included on to some extent was about three-ish years ago. So if it's changed or anybody's listening is like, this person doesn't know what he's talking about. It's changed. Much in two and a half years. This is my caveat that if it's changed, that's great. But again, I think the main emphasis that I want to make sure folks know about is the diligence on making sure we're abiding by rules and regulations that have been passed down to us is very critical for everybody who takes the job seriously.

‍

Matt:

Right. Safety. Shawn, thank you so much for your time today. This is a great conversation. Really appreciated having you on the show and really appreciated all of the insights you shared. I had a great time talking to you.

‍

Shawn:

Absolutely.

Thank you, guys. Thank you, Matt. Thank you, Stephanie. I appreciate it.

‍

Matt:

Thank you.

‍

Stephanie:

The Curiosity Current is brought to you by AYTM.

‍

Matt: